Review of Spatial and Non-spatial Data Transformation to 3D Geovisualization for Natural Disaster

2017 ◽  
pp. 340-351
Author(s):  
Muhammad Yudhi Rezaldi ◽  
Rabiah Abdul Kadir ◽  
Mohamad Taha Ijab ◽  
Azlina Ahmad
Keyword(s):  
Natural Disaster ◽  
Spatial Data ◽  
Data Transformation

A Review of Geospatial Information Technology for Natural Disaster Management in Developing Countries

2013 ◽  
pp. 175-215 ◽  
Author(s):  
Sam Herold ◽  
Michael C. Sawada
Keyword(s):  
Information Technology ◽  
Developing Countries ◽  
Natural Disaster ◽  
Disaster Management ◽  
Spatial Data ◽  
Local Level ◽  
Geospatial Information ◽  
Management Capacity ◽  
Hazard Vulnerability ◽  
Natural Disaster Management

Disasters are deadly and destructive events, particularly in developing countries where economic, social, political and cultural factors increase natural hazard vulnerability. The recent devastation of the Haiti earthquake on January 12th, 2010 was a prime example of the human toll a natural disaster can take in developing regions of the world. There is an imminent need to improve natural disaster management capacity in developing countries to reduce disaster impacts. Given that disasters are spatial phenomenon, the application of geospatial information technology (GIT) is essential to the natural disaster management process. However, in developing countries there are numerous barriers to the effective use of GIT, especially at the local level, including limited financial and human resources and a lack of critical spatial data required to support GIT use to improve disaster management related decision making processes. The results of a thorough literature review suggests that currently available free and open source GIT (FOS GIT) offers great potential to overcome some of these barriers. Thus, disaster management practitioners in developing countries could harness this potential in an attempt to reduce hazard vulnerability and improve disaster management capacity. The use of FOS GIT significantly reduces software costs and can help build local level GIT knowledge/technical skills that are required for successful GIT implementation.

scholarly journals Predicting the hurricane damage ratio of commercial buildings by claim payout from Hurricane Ike

2013 ◽  
Vol 1 (4) ◽  
pp. 3449-3483
Author(s):  
J. M. Kim ◽  
P. K. Woods ◽  
Y. J. Park ◽  
T. H. Kim ◽  
J. S. Choi ◽  
...  
Keyword(s):  
Natural Disaster ◽  
Spatial Data ◽  
Geographical Information ◽  
Insurance Companies ◽  
Economic Losses ◽  
Linear Regression Method ◽  
Financial Loss ◽  
Hurricane Damage ◽  
Vulnerability Indicators ◽  
The Individual

Abstract. The increasing occurrence of natural disaster events and related damages have led to a growing demand for models that predict financial loss. Although considerable research has studied the financial losses related to natural disaster events, and has found significant predictors, there has not yet been a comprehensive study that addresses the relationship among the vulnerabilities, natural disasters, and economic losses of the individual buildings. This study identified hurricanes and their vulnerability indicators in order to establish a metric to predict the related financial loss. We identify hurricane-prone areas by imaging the spatial distribution of the losses and vulnerabilities. This study utilized a Geographical Information System (GIS) to combine and produce spatial data, as well as a multiple linear regression method, to establish a hurricane damage prediction model. As the dependent variable, we utilized the following ratio to predict the real pecuniary loss: the value of the Texas Windstorm Insurance Association (TWIA) claim payout divided by the appraised values of the buildings. As independent variables, we selected the hurricane indicators and vulnerability indicators of the built environment and the geographical features. The developed statistical model and results can be used as important guidelines by insurance companies, government agencies, and emergency planners for predicting hurricane damage.

A Review of Geospatial Information Technology for Natural Disaster Management in Developing Countries

2012 ◽  
Vol 3 (2) ◽  
pp. 24-62 ◽  
Author(s):  
Sam Herold ◽  
Michael C. Sawada
Keyword(s):  
Information Technology ◽  
Developing Countries ◽  
Natural Disaster ◽  
Disaster Management ◽  
Spatial Data ◽  
Local Level ◽  
Geospatial Information ◽  
Management Capacity ◽  
Hazard Vulnerability ◽  
Natural Disaster Management

Disasters are deadly and destructive events, particularly in developing countries where economic, social, political and cultural factors increase natural hazard vulnerability. The recent devastation of the Haiti earthquake on January 12th, 2010 was a prime example of the human toll a natural disaster can take in developing regions of the world. There is an imminent need to improve natural disaster management capacity in developing countries to reduce disaster impacts. Given that disasters are spatial phenomenon, the application of geospatial information technology (GIT) is essential to the natural disaster management process. However, in developing countries there are numerous barriers to the effective use of GIT, especially at the local level, including limited financial and human resources and a lack of critical spatial data required to support GIT use to improve disaster management related decision making processes. The results of a thorough literature review suggests that currently available free and open source GIT (FOS GIT) offers great potential to overcome some of these barriers. Thus, disaster management practitioners in developing countries could harness this potential in an attempt to reduce hazard vulnerability and improve disaster management capacity. The use of FOS GIT significantly reduces software costs and can help build local level GIT knowledge/technical skills that are required for successful GIT implementation.

An exploratory assessment of infrastructure resilience to disasters

2020 ◽  
Vol 11 (4) ◽  
pp. 519-533
Author(s):  
Dae Woong Lee
Keyword(s):  
Natural Disaster ◽  
Spatial Data ◽  
Local Governments ◽  
Natural Hazard ◽  
Hazard Index ◽  
Disaster Risk ◽  
Content Type ◽  
Risk Levels ◽  
Infrastructure Resilience ◽  
Natural Disaster Risk

Purpose This study aims to provide an analysis and evaluation of infrastructure resilience, one of the components of disaster resilience, to natural hazards. Design/methodology/approach The analysis of this study consists of four stages. First, descriptive statistical analyses were carried out on the soft and hard infrastructure resilience and natural hazard index. Second, the spatial data were visualized through the exploratory spatial data analysis to understand the spatial distribution and spatial characteristics of variables of the data. Third, the local indicators of the spatial association method were used to identify areas in clusters where infrastructure resilience is weak. Fourth, comparisons were made between the soft and hard infrastructure resilience and natural hazard index: the level of natural hazard is high but the soft and infrastructure resilience remain very vulnerable to disaster. Findings The study found that infrastructure resilience varies from community to community, particularly in the same community, in terms of hard infrastructure and soft infrastructure. In addition, the comparative analysis between infrastructure resilience and disaster risk levels resulted in communities that were likely to suffer greatly in the event of a disaster. Originality/value This study is meaningful in that infrastructure resilience of Korean local governments was discussed by dividing them into soft and hard infrastructure and comparing them to natural disaster risk levels. In particular, the comparison with the natural disaster risk level identified local governments that are likely to experience significant damage from the natural disaster, which is meaningful in that it serves as a basis for policy practitioners to actively build infrastructure and respond to disasters.

scholarly journals Gaussian Transformation Methods for Spatial Data

Geosciences ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 196
Author(s):  
Emmanouil A. Varouchakis
Keyword(s):  
Spatial Data ◽  
Random Number Generator ◽  
Data Transformation ◽  
Rainfall Data ◽  
Public Access ◽  
Negative Sign ◽  
Alternative Methods ◽  
Positive Sign ◽  
Observation Data ◽  
Transformation Methods

Data gaussianity is an important tool in spatial statistical modeling as well as in experimental data analysis. Usually field and experimental observation data deviate significantly from the normal distribution. This work presents alternative methods for data transformation and revisits the applicability of a modified version of the well-known Box-Cox technique. The recently proposed method has the significant advantage of transforming negative sign (fluctuations) data in advance to positive sign ones. Fluctuations derived from data detrending cannot be transformed using common methods. Therefore, the Modified Box-Cox technique provides a reliable solution. The method was tested in average rainfall data and detrended rainfall data (fluctuations), in groundwater level data, in Total Organic Carbon wt% residuals and using random number generator simulating potential experimental results. It was found that the Box-Cox technique competes successfully in data transformation. On the other hand, it improved significantly the normalization of negative sign data or fluctuations. The coding of the method is presented by means of a Graphical User Interface format in MATLAB environment for reproduction of the results and public access.

scholarly journals Asessment of flood risk on human in downstream of Vu Gia - Thu Bon river basin by combining GIS and hydraulic model

2020 ◽  
Vol 61 (4) ◽  
pp. 46-56
Author(s):  
Quy Ngoc Bui ◽  
Nga Thi Le ◽  
Quan Anh Duong ◽  
Anh Mai Thi Tran ◽  
Keyword(s):  
Natural Disaster ◽  
River Basin ◽  
Spatial Data ◽  
Flood Risk ◽  
Assessment Method ◽  
Economic Activities ◽  
Attribute Information ◽  
Gis Software ◽  
The Impact ◽  
Flooded Areas

Flooding is a natural disaster that occurs in most parts of Vietnam, causing a lot of damage to people, assets and influence on socio-economic activities. This article analyzes the impact of floods on population in the Vu Gia – Thu basin. In this study, the authors used the result of the hydrolic modeling, the simulated floods in conjunction with the use of GIS software (ArcGIS) to analyze the impact on the population of downstream the Vu Gia – Thu Bon river basin. From there, the study indicated the impact and influence of floods caused on flooded areas in the river basin. The ArcGIS software is used to prepare data, integrate and analyze the spatial data that is included with the attribute information. This study show that GIS provides more accurate solutions for flood impact assessment and this assessment method can be applied to any river basin.

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